The context in which the data is collected can also create spurious correlations between groups of images.
Different acquisition situations may provide different contextual information that can generate systematic artifacts in specific kinds of images.
For instance, confounding variables such as the snowy background in the Wolves versus Huskies example of [1]} (see Section ) may add bias in algorithmic decisions. In this case, different objects in images may have similar features due to the presence of a similar context, such as the color background, which can play an important role in the classification task due to spurious correlations. We refer to [2]} for more references. This phenomenon is also well known in biology where spectroscopy data are highly influenced by the fluorescence methods as highlighted in [3]}, which makes machine learning difficult to use without correcting the bias. Different biases related to different instruments of measures are also described for medical data in [4]}.
